Proving ROI has never been easy. Marketers are under pressure to show how their work contributes to the business, ideally with a spreadsheet-ready answer. If not, it's bye-bye budget.

There are more data and tools at their disposal than ever before, yet many still struggle to explain what's really driving performance. An Intuit Mailchimp survey of UK marketers and business decision-makers found that 42% worry they're wasting budget because they lack visibility into results, while 39% of business leaders say they don't have a clear view of which marketing activity is actually making a difference. In other words, the data is there - the clarity isn't.

ROI today is less about measurement and more about explanation. Metrics like clicks and engagement can help with optimisation, but they don't always explain what marketing is actually doing for the business. Add AI and automation into the mix, and it only makes things more complicated.

Why the ‘so what?’ matters

It's easy to fall into the same old trap with ROI reporting. First comes the dashboards, then the metrics, and only after all that does the real question arise: so what?

As Julaine Speight, First Internet co-owner and marketing director, says: "Senior stakeholders are far less interested in volume for volume's sake." There's always someone who still wants the big headline numbers, but those topline metrics: impressions, likes, and raw traffic, are now much less relevant unless it's obvious why they matter. 

At board level, revenue and efficiency tend to matter most, as they show if marketing is helping the business grow: "The metrics that consistently resonate at board level are those tied directly to revenue and efficiency: customer acquisition cost, marketing-influenced revenue, pipeline value, conversion rates by stage, and customer lifetime value," says Speight. 

There's also a growing demand for simplicity. Senior leaders don't want more dashboards. They want clearer answers: "The marketers who win trust are the ones who simplify the story without dumbing it down," Speight adds.

Why your CEO doesn't care about your CTR

Even when you get the metrics right, there can still be a gap between what gets reported and what senior management actually cares about. This is especially true with channel-level metrics that look good on paper but don't mean much in the boardroom. Wes Maynard, managing director at MTM Agency, explains: "What marketers measure and what CEOs actually need to know are almost never the same thing, especially when it comes to ROI. Your CEO doesn't care about your click-through rate. They care about whether marketing is contributing to the company's ability to hit revenue targets, expand market share, and improve profitability." 

A broader view of ROI tends to land better, one that looks at performance alongside what's happening with the brand: "I believe the blending of hard and soft data will set you aside from the rest when it comes to reporting ROI in 2026 and beyond," says Maynard. Things like brand health and customer sentiment can feel harder to measure, but they often help explain why performance changes over time. When trust improves, results usually follow.

AI is making these connections easier to see: "In 2026, AI-driven models are being used to correlate brand health movements with commercial outcomes over time, revealing patterns that last-click attribution could never capture," Maynard explains. When used well, it's this kind of analysis that helps leaders see which investments are actually paying off in the long run, not just delivering short-term spikes.

To win that all-important trust from those above, marketers need to be able to explain why what they're doing now will matter later: "The marketers earning executive trust in 2026 are the ones who can stand in front of the board and explain how brand investment today creates commercial opportunity tomorrow, and back it up with both hard revenue data and credible brand health indicators," he says. 

Don't leave ROI to the end

ROI reporting often feels painful for one simple reason: it's left too late. The campaign is planned, it goes live, and only then does the all-important question get asked: how do we know it's worked? By that point, the options are usually pretty limited.

Laura Rudolph, strategy director at Leopard Co, says this problem often starts at the planning stage.

"To make our agency's 'purposeful' value a reality in our everyday work, we have created the Leopard Co Results Framework with the purpose of baking the 'why' into every stage of our planning, not added at the end."

The emphasis, she explains, is on being clear about intent from the very start: "Importantly, with the Results Framework, we get to the heart of why a client is investing in an activity, and what 'good' really looks like - moving conversations from tactics to outcomes."

That clarity matters. When you know what success looks like early on, it's much easier to understand the results further down the line.

She also warns against assuming that more technology and tools are the answer: "Whilst we are always on the lookout for new tools to support our work, we know that having too many disconnected tools creates inefficiencies," Rudolph says. "Our emphasis is on ensuring our current tools work together effectively to provide a unified view of performance and customer behaviour."

That joined-up view becomes more important as marketers move away from campaign-by-campaign reporting: "As we move into the next stage of our measurement journey, our focus is on evolving from campaign reporting to true commercial modelling," Rudolph explains. 

Using AI to improve ROI

Rather than treating channels and campaigns as separate activities, AI can make it easier to understand how customers move across touchpoints over time - and where value is actually created.

Diana Williams, VP of product at Intuit Mailchimp, says a lot of the opportunity lies in making better use of data brands already have: "Most retailers already have powerful data sitting right inside their e-commerce platforms - things like what a customer bought, how often they purchase, and even which products they viewed but didn't buy."

And using that data to personalise messaging is quickly becoming expected rather than optional. AI does the heavy lifting by connecting signals that are easy to miss: "One of the biggest benefits of AI is that it enables marketers to layer these cues to identify their best customers, understand what keeps them coming back, and predict when they're likely to re-engage," Williams explains.

It's also more efficient. In a market where "39% of shoppers say they're overwhelmed by constant promotions", relevance matters. As Williams puts it, "we'll see marketers and consumers both benefit from an approach that harnesses AI to deliver smarter allocation of effort, creativity, and spend." 

Attribution doesn't need to be perfect

Even with all this data and technology at our fingertips, attribution remains one of the hardest parts of ROI reporting. 

As Jaimie Patel, senior vice president of marketing science at Critical Mass, says: "It's always been a challenge to assign accurate revenue attribution to marketing, especially when it comes to qualitative drivers like brand experience, NPS and customer sentiment." Then you've got to think about costs and multiple touchpoints on top of that, and it can all become very frustrating.

What's changing is how marketers work with imperfect data: "Modern data modelling and AI-powered processing enable meaningful correlation across imperfect datasets," Patel explains, meaning that "precision matters less than directional accuracy once algorithms can accurately surface patterns, connectivity, and impact at scale."

This changes what ROI can realistically capture: "Marketing ROI will soon include the ability to accurately track direct revenue impact alongside the full spectrum of marketing influence, previously intangible and less connected factors, and attribution models directly connecting customer experience and engagement with the marketing value chain," Patel says.

The result is ROI that's actually useful. Patel describes it evolving "from a backward-looking financial calculation into a forward-looking decision metric to shape strategy, investment, and accountability across marketing."

What good ROI reporting looks like in 2026

Good ROI reporting isn't about how much data you have. It's about how clearly you can explain what's going on. Start by asking the right questions and then use the data to answer them in a way that makes sense to the business.

It puts numbers into context, helping people see how all the different strands of marketing activity connect over time rather than viewing everything in isolation. It also accepts that you won't have a neat answer for everything.

But, most importantly, good ROI reporting is written for people, not platforms. It’s there to help the business understand what’s working, what isn’t and what needs to change.

Want to upskill your knowledge on ROI to get ahead in 2026? CIM’s Data and Marketing Analytics training course offers a practical guide to using data analytics and AI to optimise marketing ROI.